Estimating the mirror seeing for a large optical telescope with a numerical method
Bibliographic record
Abstract
Abstract It is widely accepted that mirror seeing is caused by turbulent fluctuations in the index of air refraction in the vicinity of a telescope mirror. Computational Fluid Dynamics (CFD) is a useful tool to evaluate the effects of mirror seeing. In this paper, we present a numerical method to estimate the mirror seeing for a large optical telescope (∼ 4 m) in cases of natural convection with the ANSYS ICEPAK software. We get the FWHM of the image for different inclination angles ( i ) of the mirror and different temperature differences (Δ T ) between the mirror and ambient air. Our results show that the mirror seeing depends very weakly on i , which agrees with observational data from the Canada-France-Hawaii Telescope. The numerical model can be used to estimate mirror seeing in the case of natural convection although with some limitations. We can determine Δ T for thermal control of the primary mirror according to the simulation, empirical data and site seeing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".